High throughput whole genome platforms to quantitatively and efficiently assay human DNA methylation provide an extraordinary opportunity to accelerate progress in the identification of epigenetic mechanisms for complex human autoimmune diseases such as Sjogren's Syndrome (SS). Epigenetic modifications, such as methylation of the 5' carbon of cytosine which occurs in the context of CpG dinucleotides, do not affect the genetic sequence; however, they do play a critical role in transcriptional regulation of a gene and subsequent gene expression. We have applied this technology, in conjunction with whole genome SNP genotyping to a clinically well-characterized collection of SS cases and controls developed specifically for genetic and epigenetic studies. Samples from the Sjogren's International Collaborative Clinical Alliance (SICCA) repository (total n=3,500 individuals) will b utilized, given the careful phenotype characterization and standardized collection of biospecimens that has been performed for each participant. For this proposal, we have initially obtained DNA from peripheral blood mononuclear cells (PBMCs) and sorted cell populations with high purity, including CD14+ monocytes, CD19+ B cells, CD4+ T cells, as well as labial salivary gland biopsy tissue from 120 SICCA participants (100 SS cases and 20 controls). Data collected for ~450,000 highly informative CpG sites spanning 22,000 genes across the genome will provide a comprehensive assessment of DNA methylation for each individual for our proposed study Aims. As part of Aim 1, DNA methylation profiles will be examined using three main approaches: 1) Global DNA methylation profiles will be analyzed for association with SS risk in all sample types; 2) Multidimensional scaling analysis (MDS) and principal components analysis (PCA) will be performed to determine whether SS case samples can be distinguished from control samples; and 3) Local analyses will also be performed and will focus specifically on DNA methylation changes within candidate SS genes and genes demonstrating at least 1.5 fold methylation differences between cases and controls. Our proposal will take advantage of available whole genome data for each study participant and results from the largest genome wide association study (GWAS) in SS. In Aim 2, we will utilize DNA methylation profiles and methods from Aim 1 to determine whether important SS case phenotypes can be distinguished from one another. We will identify top ranked CpG sites that are either hypo or hyper methylated in specific SS case groups or that distinguish cases from controls using supervised machine learning algorithms. We will attempt replication for all top findings in an independent dataset, followed by random effects meta-analysis to incorporate other SS risk factors. Finally, we will determine whether DNA methylation profiles of interest from Aims 1 and 2 can be detected in DNA from whole blood for potential use as a biomarker. The identification of unique epigenetic profiles in SS, and an understanding of these profiles in the context of different cell and tissue types and genetic background have the potential to significantly transform our understanding of SS etiology.